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Aug 7
Sand Technologies
The energy sector has long been data-intensive, with information flowing from exploration fields to refineries and trading desks. Oil and gas companies, for instance, generate large volumes of operational data from sources like seismic imaging, drilling logs, pipeline telemetry and real-time market feeds. But despite the volume, data is often trapped in disconnected systems, forcing teams to make critical operational and commercial decisions with incomplete or delayed information.
For many operators, the challenge isn’t collecting more data, but rather, connecting the right data across systems and teams. When oil and gas companies move beyond fragmented workflows and slow data pipelines, they gain the ability to respond faster, improve efficiency and support more sustainable.
Energy operations generate more data than ever before. A single offshore platform, for instance, can produce up to 15 petabytes over its lifetime, powered by more than 80,000 sensors. As data volumes grow, the real change lies in how its been used to drive more precise, efficient and responsive operations.
Exploration and production are among the most high-stakes stages of the oil and gas value chain. Every drilling decision carries significant operational and financial risk. To reduce uncertainty and improve outcomes, companies are turning to AI and advanced analytics in various upstream operations.
Firstly, to better understand the subsurface, companies are leveraging seismic imaging, well logs and core samples which map out reservoir characteristics like porosity and fluid content. These insights guide drilling strategies and reduce the risk of dry wells. For example, BP uses advanced seismic analytics to improve accuracy in offshore exploration zones. The technology which analyzes data in weeks (this would typically take a year) has helped the compay uncover new oil reserves within existing fields that might have otherwise been missed.
In drilling, AI and advanced analytics are enabling operators to act on real-time telemetry data such as pressure, flow rate and temperature from thousands of sensors. These tools support live adjustments that enhance well performance and reduce downtime. Shell, for instance, leverages reinforcement learning, an AI approach where systems learn by trial and error to to support real-time drilling decisions. Trained on historical and simulated data, these systems help geosteering teams improve well placement, minimize downtime and extend equipment lifespan.
AI-enabled predictive maintenance is also being used to transform equipment reliability in upstream operations. By applying machine learning to sensor data, operators are able to catch early warning signs and intervene sooner. Companies such as Total Energies have leveraged similar predictive maintenance systems to detect equipment failures. Research shows that predictive maintenance can lead to a 25% increase in production, 20% reduction in maintenance costs and a 45% decrease in equipment downtime.
In the midstream segment, the safe and efficient transport of oil and gas depends on constant visibility across pipelines, terminals and storage facilities. AI and data-driven technologies are improving how operators monitor flow rates, track product movement and respond to anomalies.
Industrial IoT sensors combined with edge computing and MQTT protocols enable real-time tracking of pressure, temperature and vibration across pipeline infrastructure. These tools help detect leaks early, prevent spills and minimize safety risks. Enbridge, for example, has deployed AI-powered leak detection systems that analyze live and historical data to catch anomalies faster than traditional methods.
Integrated data across transport routes and storage terminals is also helping midstream operators improve inventory management and logistics. Companies such as Kinder Morgan are using data analytics to monitor and optimize storage capacity across its terminal network. With clearer visibility into product volumes and flow constraints, they can make faster, more accurate decisions around scheduling, rerouting and inventory.
Refining and fuel delivery operate on tight margins. Small gains in efficiency or delivery speed can have outsized impact. That’s why operators are leaning on real-time data to optimize both refining processes and distribution networks.
Inside refineries, advanced control systems continuously monitor process data to adjust variables like temperature, pressure and chemical flow. These adjustments help maximize output while reducing both energy consumption and emissions. Repsol has leveraged similar systems powered by advanced analytics and AI at its Tarragona refinery to improve energy efficiency and operational performance. This initiative is projected to increase fuel margins by up to $20 million each year.
On the logistics side, combining data from demand forecasts, transportation costs and delivery routes is enabling faster, more informed decision-making. This integrated approach has helped companies have more more efficient routing, lower fuel consumption and improve overall supply chain flexibility.
Energy procurement is another downstream area being transformed by data and automation. Once a manual and time-consuming process, is now powered by AI tools that track usage patterns, market prices and contract terms. These systems generate generate optimal schedules, flag risks and even automate transactions, boosting cost efficiency and agility in volatile markets.
Operational platforms like SCADA, ERP, ETRM and GIS often operate in silos, using different formats and data definitions. This fragmentation makes it difficult to align data across assets, teams, and decision-making layers, limiting end-to-end visibility and coordination.
Moving toward modular, cloud-compatible architecture with standardised data models can help reduce this fragmentation. Similarly, layering integration platforms that sync and translate data across systems enable a clearer, more connected view of operations without disrupting core workflows.
Even when data is available, it’s often incomplete, inconsistent or delayed. This affects everything from production planning to emissions tracking and compromises safety and compliance reporting. In fast-moving operational environments, lagging or unreliable data creates unnecessary risk.
Improving data quality starts with clearer ownership and consistent standards which entails defining what “good” data looks like across functions. Many companies are also automating data validation at the source and enriching it with metadata, so issues are caught early. Combined with real-time processing at the edge, this ensures faster, more reliable data flows where they’re needed most, whether for safety systems, emissions reporting or operational planning.
Legacy systems, used by many energy companies add another layer of complexity as they often are not designed for the demands of modern analytics. These systems struggle to support the volume and speed required for real-time decisions or AI deployment. The result is limited scalability and slow progress on digital initiatives that rely on cross-functional insight.
Overcoming the limits of legacy systems doesn’t always require a full rebuild. Many companies are taking a phased approach and using middleware, APIs and data lakes to extend the capabilities of older platforms without disrupting operations. By isolating key data flows and shifting them to scalable cloud infrastructure, organizations can enable real-time analytics and AI without replacing legacy systems.
Many organizations lack clear ownership of data assets. This leads to duplicated efforts, inconsistent standards and underuse of available tools. At the same time, low data literacy among frontline teams, combined with rising cybersecurity threats, makes operationalizing data even more complex.
Clear data ownership and consistent governance frameworks reduce duplication and misalignment. Complementing this with targeted upskilling, embedding analytics into daily workflows and security protocols also helps teams use data more confidently and safely in daily operations.
Data is already everywhere in energy operations but the real value comes from making it work across systems, teams and decisions. When companies move past siloed tools and slow data pipelines, they gain the speed and visibility needed to improve performance and reduce risk. The most effective gains come not from collecting more data, but from connecting the right data and using it in real time.
This shift also extends to how companies track their environmental performance. As one of the world’s largest sources of greenhouse gas emissions, accounting for nearly 75% of global emissions, the industry faces growing pressure to monitor and reduce its footprint. Metrics like emissions, flare volumes and equipment leaks are now tracked with the same urgency as output and uptime.
As a result, energy operators need to rethink how they measure their environmental impact, how frequently they report on it and what steps they’re taking to improve it. New regulations are also pushing for more transparency, making it critical for operators to integrate operational data with ESG reporting to stay ahead. In this changing landscape, data is not just another everyday tool, but its becoming a strategic asset for building for shaping a more resilient and responsible energy industry.
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